Practical applications of the hydrogen evolution reaction (HER) rely on the development of highly efficient, stable, and low-cost catalysts. Tuning the electronic structure, morphology, and architecture of catalysts is an important way to realize efficient and stable HER electrocatalysts. Herein, Co-doped Cu3P-based sugar-gourd structures (CoâCu3P/CF) are prepared on copper foam as active electrocatalysts for hydrogen evolution. This hierarchical structure facilitates fast mass transport during electrocatalysis. Notably, the introduction of Co not only induces a charge redistribution but also leads to lattice-mismatch on the atomic scale, which creates defects and performs as additional active sites. Therefore, CoâCu3P/CF requires an overpotential of only 81, 111, 185, and 230 mV to reach currents of 50, 100, 500, and 1000 mA cm-2 in alkaline media and remains stable after 10 000 CV cycles in a row and up to 110 h i-t stability tests. In addition, it also shows excellent HER performance in water/seawater electrolytes of different pH values. Experimental and DFT show that the introduction of Co modulates the electronic and energy level structures of the catalyst, optimizes the adsorption and desorption behavior of the intermediate, reduces the water dissociation energy barrier during the reaction, accelerates the Volmer step reaction, and thus improves the HER performance.
Heteroatom doping and heterostructure construction are the key methods to improve the performance of electrocatalysts. However, developing such catalysts remains a challenging task. Herein, we designed two comparable polymers, phytic acid/thiourea polymer (PATP) and phytic acid/urea polymer (PAUP), as precursors, which contain C, N, S/O, and P by microwave heating. To pinpoint how the introduction of sulfur would affect the electronic structure and catalytic activity, these two polymers were physically blended with CoCo-Prussian blue analogue (CoCo-PBA) and further calcination, respectively. The highly dispersed CoP/Co2P-rich interfacial catalysts anchored on the N,S-codoped or N-doped carbon support were successfully prepared (CoP/Co2P@CNS and CoP/Co2P@CN). The prepared CoP/Co2P@CNS catalyst showed good ORR properties (E1/2 = 0.856 V vs RHE) and OER properties (Ej10 = 1.54 V vs RHE), which were superior to the commercial Pt/C and RuO2 catalysts. The reversible oxygen electrode index (ΔE = Ej10 - E1/2) can reach â¼0.684 V. Meanwhile, the rechargeable zinc-air battery assembled with a CoP/Co2P@CNS catalyst as the air cathode also showed excellent performance, with a charge-discharge cycle stability of up to 900 h. DFT calculations further confirm that the introduction of S atoms can affect the electronic structure and enhance the catalytic activity of C and N atoms on carbon support.
Developing novel supercapacitor electrodes with high energy density and good cycle stability has aroused great interest. Herein, the vertically aligned CoNiO2/Co3O4 nanosheet arrays anchored on boron doped diamond (BDD) films are designed and fabricated by a simple one-step electrodeposition method. The CoNiO2/Co3O4/BDD electrode possesses a large specific capacitance (214 mF cm-2) and a long-term capacitance retention (85.9% after 10,000 cycles), which is attributed to the unique two-dimensional nanosheet architecture, high conductivity of CoNiO2/Co3O4 and the wide potential window of diamond. Nanosheet materials with an ultrathin thickness can decrease the diffusion length of ions, increase the contact area with electrolyte, as well as improve active material utilization, which leads to an enhanced electrochemical performance. Additionally, CoNiO2/Co3O4/BDD is fabricated as the positive electrode with activated carbon as the negative electrode, this assembled asymmetric supercapacitor exhibits an energy density of 7.5 W h kg-1 at a power density of 330.5 W kg-1 and capacity retention rate of 97.4% after 10,000 cycles in 6 M KOH. This work would provide insights into the design of advanced electrode materials for high-performance supercapacitors.
Environmentally friendly electrocatalytic coupling of CO2 and N2 for urea synthesis is a promising strategy. However, it is still facing problems such as low yield as well as low stability. Here, a new carbon-coated liquid alloy catalyst, Ga79Cu11Mo10@C is designed for efficient electrochemical urea synthesis by activating Ga active sites. During the N2 and CO2 co-reduction process, the yield of urea reaches 28.25â mmol h-1 g-1, which is the highest yield reported so far under the same conditions, the Faraday efficiency (FE) is also as high as 60.6 % at -0.4â V vs. RHE. In addition, the catalyst shows excellent stability under 100â h of testing. Comprehensive analyses showed that sequential exposure of a high density of active sites promoted the adsorption and activation of N2 and CO2 for efficient coupling reactions. This coupling reaction occurs through a thermodynamic spontaneous reaction between *N=N* and CO to form a C-N bond. The deformability of the liquid state facilitates catalyst recovery and enhances stability and resistance to poisoning. Moreover, the introduction of Cu and Mo stimulates the Ga active sites, which successfully synthesises the *NCON* intermediate. The reaction energy barrier of the third proton-coupled electron transfer process rate-determining step (RDS) *NHCONHâ*NHCONH2 was lowered, ensuring the efficient synthesis of urea.
OBJECTIVES: Idiopathic inflammatory myopathies (IIMs) are a group of heterogeneous autoimmune diseases. Intron retention (IR) serves as an important post-transcriptional and translational regulatory mechanism. This study aims to identify changes in IR profiles in IIM subtypes, investigating their influence on proteins and their correlations with clinical features. METHODS: RNA sequencing and liquid chromatography-tandem mass spectrometry were performed on muscle tissues obtained from 174 patients with IIM and 19 controls, following QC procedures. GTFtools and iREAD software were used for IR identification. An analysis of differentially expressed IRs (DEIs), exons and proteins was carried out using edgeR or DEP. Functional analysis was performed with clusterProfiler, and SPIRON was used to assess splicing factors. RESULTS: A total of 6783 IRs located in 3111 unique genes were identified in all IIM subtypes compared with controls. IIM subtype-specific DEIs were associated with the pathogenesis of respective IIM subtypes. Splicing factors YBX1 and HSPA2 exhibited the most changes in dermatomyositis and immune-mediated necrotising myopathy. Increased IR was associated with reduced protein expression. Some of the IIM-specific DEIs were correlated with clinical parameters (skin rash, MMT-8 scores and muscle enzymes) and muscle histopathological features (myofiber necrosis, regeneration and inflammation). IRs in IFIH1 and TRIM21 were strongly correlated with anti-MDA5+ antibody, while IRs in SRP14 were associated with anti-SRP+ antibody. CONCLUSION: This study revealed distinct IRs and specific splicing factors associated with IIM subtypes, which might be contributing to the pathogenesis of IIM. We also emphasised the potential impact of IR on protein expression in IIM muscles.
Rolling shutter temporal super-resolution (RSSR), which aims to synthesize intermediate global shutter (GS) video frames between two consecutive rolling shutter (RS) frames, has made remarkable progress with the development of deep convolutional neural networks over the past years. Existing methods cascade multiple separated networks to sequentially estimate intermediate motion fields and synthesize target GS frames. Nevertheless, they are typically complex, do not facilitate the interaction of complementary motion and appearance information, and suffer from problems such as pixel aliasing or poor interpretation. In this paper, we derive the uniform bilateral motion fields for RS-aware backward warping, which endows our network a more explicit geometric meaning by injecting spatio-temporal consistency information through time-offset embedding. More importantly, we develop a unified, single-stage RSSR pipeline to recover the latent GS video in a coarse-to-fine manner. It first extracts pyramid features from given inputs, and then refines the bilateral motion fields together with the anchor frame until generating the desired output. With the help of our proposed bilateral cost volume, which uses the anchor frame as a common reference to model the correlation with two RS frames, the gradually refined anchor frames not only facilitate intermediate motion estimation, but also compensate for contextual details, making additional frame synthesis or refinement networks unnecessary. Meanwhile, an asymmetric bilateral motion model built on top of the symmetric bilateral motion model further improves the generality and adaptability, yielding better GS video reconstruction performance. Extensive quantitative and qualitative experiments on synthetic and real data demonstrate that our method achieves new state-of-the-art results.
PURPOSE: Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment. METHODS: We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls. RESULTS: We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC. CONCLUSION: Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.
Breast Neoplasms , MicroRNAs , Humans , Female , MicroRNAs/genetics , Breast Neoplasms/diagnosis , Breast Neoplasms/genetics , Breast Neoplasms/pathology , Gene Expression Profiling , Biomarkers, Tumor/genetics , Liquid Biopsy
High entropy materials offer almost unlimited catalytic possibilities due to their variable composition, unique structure, and excellent electrocatalytic performance. However, due to the strong tendency of nanoparticles to coarsen and agglomerate, it is still a challenge to synthesize nanoparticles using simple methods to precisely control the morphology and size of the nanoparticles in large quantities, and their large-scale application is limited by high costs and low yields. Herein, a series of high-entropy oxides (HEOs) nanoparticles with high-density and ultrasmall size (<5 nm) loaded on carbon nanosheets with large quantities are prepared by Joule-heating treatment of gel precursors in a short period of time (≈60 s). Among them, the prepared (FeCoNiRuMn)3 O4-x catalyst shows the best electrocatalytic activity for oxygen evolution reaction, with low overpotentials (230 mV @10 mA cm-2 , 270 mV @100 mA cm-2 ), small Tafel slope (39.4 mV dec-1 ), and excellent stability without significant decay at 100 mA cm-2 after 100 h. The excellent performance of (FeCoNiRuMn)3 O4-x can be attributed to the synergistic effect of multiple elements and the inherent structural stability of high entropy systems. This study provides a more comprehensive design idea for the preparation of efficient and stable high entropy catalysts.
Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.
Drug response prediction is essential for drug development and disease treatment. One key question in predicting drug response is the representation of molecules, which has been greatly advanced by artificial intelligence (AI) techniques in recent years. In this review, we first describe different types of representation methods, pinpointing their key principles and discussing their limitations. Thereafter we discuss potential ways how these methods could be further developed. We expect that this review will provide useful guidance for researchers in the community.
Artificial Intelligence , Pharmaceutical Preparations
The novel design of carbon materials with stable nanoarchitecture and optimized electrical properties featuring simultaneous intercalation of lithium ions (Li+ ) and sodium ions (Na+ ) is of great significance for the superb lithium- sodium storage capacities. Biomass-derived carbon materials with affluent porosity have been widely studied as anodes for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). However, it remains unexplored to further enhance the stability and utilization of the porous carbon skeleton during cycles. Here, a lotus stems derived porous carbon (LPC) with graphene quantum dots (GQDs) and intrinsic carbon nanowires framework (CNF) is successfully fabricated by a self-template method. The LPC anodes show remarkable Li+ and Na+ storage performance with ultrahigh capacity (738 mA h g-1 for LIBs and 460 mA h g-1 for SIBs at 0.2 C after 300 cycles, 1C≈372 mA h g-1 ) and excellent long-term stability. Structural analysis indicates that the CNFs-supported porous structure and internal GQDs with excellent electrical conductivity contribute significantly to the dominant capacitive storage mechanism in LPC. This work provides new perspectives for developing advanced carbon-based materials for multifunctional batteries with improved stability and utilization of porous carbon frameworks during cycles.
Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimers Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data.
Alzheimer Disease , Neuroimaging , Humans , Neuroimaging/methods , Alzheimer Disease/diagnostic imaging , Alzheimer Disease/genetics , Image Interpretation, Computer-Assisted/methods , Databases, Factual
Introduction: Sepsis is a vitally serious disease leading to high mortality. Nucleated red blood cells (NRBCs) are present in some noninfectious diseases, but the relationship between NRBCs and sepsis in children remains unknown. The purpose of this study was to compare the clinical characteristics and outcomes of sepsis with positive NRBCs and negative NRBCs in children, and to further explore whether the count of NRBCs has a relationship with the severity of sepsis. Methods: We enrolled children with sepsis who were admitted to the Children's Hospital of Chongqing Medical University between January 2020 and December 2022. The children's clinical data, laboratory data and outcomes were recorded and analyzed. Results: One hundred and fifteen children met the inclusion criteria in our study. Compared to negative NRBCs patients, the C-reactive protein, alanine transaminase, urea nitrogen values, mortality rate and length of hospitalization were found to be significantly increased, while platelet counts, and hemoglobin were significantly decreased in sepsis patients with positive NRBC (P < 0.05). Receiver operating characteristic (ROC) curves analysis showed that the optimal cutoff value of the NRBC count in the diagnosis of severe sepsis was 3, with a sensitivity of 87.5% and specificity of 94.9%. The area under the ROC curve was 0.877 (95% CI: 0.798-0.957). Discussion: These findings demonstrated that NRBC count has the potential to be a biomarker for the diagnosis of sepsis in children, especially an NRBC count greater than 3, which may predict the severity and poor prognosis in children suffering from sepsis.
Erythroblasts , Sepsis , Humans , Child , Biomarkers , C-Reactive Protein , Hospitalization , Sepsis/diagnosis
The high-performance determination of heavy metal ions (Cd2+) in water sources is significant for the protection of public health and safety. We have developed a novel sensor of nanograss boron and nitrogen co-doped diamond (NGBND) to detect Cd2+ using a simple method without any masks or reactive ion etching. The NGBND electrode is constructed based on the co-doped diamond growth mode and the removal of the non-diamond carbon (NDC) from the NGBND/NDC composite. Both the enlarged surface area and enhanced electrochemical performance of the NGBND film are achievable. Scanning electron microscopy, Raman spectroscopy, X-ray photoelectron spectroscopy, cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse anodic stripping voltammetry (DPASV) were used to characterize the NGBND electrodes. Furthermore, we used a finite element numerical method to research the current density near the tip of NGBND. The NGBND sensor exhibits significant advantages for detecting trace Cd2+ via DPASV. A broad linear range of 1 to 100 µg L-1 with a low detection limit of 0.28 µg L-1 was achieved. The successful application of this Cd2+ sensor indicates considerable promise for the sensitive detection of heavy metal ions.
Single-cell clustering is a critical step in biological downstream analysis. The clustering performance could be effectively improved by extracting cell-type-specific genes. The state-of-the-art feature selection methods usually calculate the importance of a single gene without considering the information contained in the gene expression distribution. Moreover, these methods ignore the intrinsic expression patterns of genes and heterogeneity within groups of different mean expression levels. In this work, we present a Feature sElection method based on gene Expression Decomposition (FEED) of scRNA-seq data, which selects informative genes to enhance clustering performance. First, the expression levels of genes are decomposed into multiple Gaussian components. Then, a novel gene correlation calculation method is proposed to measure the relationship between genes from the perspective of distribution. Finally, a permutation-based approach is proposed to determine the threshold of gene importance to obtain marker gene subsets. Compared with state-of-the-art feature selection methods, applying FEED on various scRNA-seq datasets including large datasets followed by different common clustering algorithms results in significant improvements in the accuracy of cell-type identification. The source codes for FEED are freely available at https://github.com/genemine/FEED.
Gene Expression Profiling , Single-Cell Analysis , Gene Expression Profiling/methods , Sequence Analysis, RNA/methods , Single-Cell Analysis/methods , Algorithms , Cluster Analysis , Gene Expression
In this work, the structural, electronic and mechanical properties of fluorinated diamane (F-diamane) with N and B dopants are systemically investigated using first-principles calculation. The N atom tends to stay in the external substituted site without F saturation, while the B-doped structure of the substituted external site with F saturation is the most stable. Ab initio molecular dynamics simulations confirm the thermal stability. The band structures of stable doped structures are similar to that of pristine F-diamane, due to the slight contribution of the dopant to the band edges. In addition, after the introduction of the B dopant, the formation energy reduces, and the transition barrier of graphene bilayers into diamane is smaller, indicating the feasibility of graphene bilayer fluoridation. Furthermore, we find that these doped structures have mechanical stability with isotropic elastic constants, Young's modulus, shear modulus and Poisson's ratio. Our work would promote the synthesis and development of two-dimensional diamane.
MOTIVATION: A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider the relevance of each feature to specific functions and ignore the noise caused by the irrelevant features. In this case, we hypothesize that constructing a feature selection framework to extract the function-relevant features might help improve the model accuracy in isoform function prediction. RESULTS: In this article, we present a feature selection-based approach named IsoFrog to predict isoform functions. First, IsoFrog adopts a reversible jump Markov Chain Monte Carlo (RJMCMC)-based feature selection framework to assess the feature importance to gene functions. Second, a sequential feature selection procedure is applied to select a subset of function-relevant features. This strategy screens the relevant features for the specific function while eliminating irrelevant ones, improving the effectiveness of the input features. Then, the selected features are input into our proposed method modified domain-invariant partial least squares, which prioritizes the most likely positive isoform for each positive MIG and utilizes diPLS for isoform function prediction. Tested on three datasets, our method achieves superior performance over six state-of-the-art methods, and the RJMCMC-based feature selection framework outperforms three classic feature selection methods. We expect this proposed methodology will promote the identification of isoform functions and further inspire the development of new methods. AVAILABILITY AND IMPLEMENTATION: IsoFrog is freely available at https://github.com/genemine/IsoFrog.
Alternative Splicing , Machine Learning , Markov Chains , Protein Isoforms , Monte Carlo Method
Blue light (BL) irradiation has been a potentially efficient treatment for many kinds of tumors. In this study, a BL irradiation (centered at 453 nm in wavelength) was proposed to treat the common human liver cancer cell lines of SMMC-7721 and HepG2, examined by means of flow cytometry, western blot, fluorescence microscope assay. In comparison to control groups, the apoptosis and proliferation inhibition of both BL-treated cells are expressively enhanced by mitochondrial apoptosis. The mechanism of apoptosis is related to the more production of reactive oxygen species (ROS) induced by BL and the corresponding changes in the expression of apoptosis-related Bcl-2, Bax and Bad proteins. In addition, the migration rate of the cancer cells could be reduced after BL irradiation. These results demonstrate that introducing BL irradiation is helpful to establish an effective and low toxicity strategy for the clinical treatment of liver tumors.
Apoptosis , Liver Neoplasms , Humans , Liver Neoplasms/drug therapy , Light , Cell Line, Tumor , Cell Proliferation , Reactive Oxygen Species/metabolism
Aerogels, as three-dimensional porous materials, have attracted much attention in almost every field owing to their unique structural properties. Designing high-entropy alloy aerogels (HEAAs) to quinary and above remains an enormous challenge due to the different reduction potentials and nucleation/growth kinetics of different constituent metals. Herein, a novel and universal chelating co-reduction strategy to prepare HEAAs at room temperature in the water phase is proposed. The addition of chelators (ethylenediaminetetraacetic acid tetrasodium salt, sodium citrate, salicylic acid, and 4,4'-bipyridine) with a certain strong coordination capacity can adjust the reduction potential of different metal components, which is the key to synthesize single-phase solid solution alloys successfully. The optimized AgRuPdAuPt HEAA can be an excellent electrocatalyst for hydrogen evolution reaction (HER) with an ultrasmall overpotential of 22 mV at 10 mA cm-2 and excellent stability for 24 h in an alkaline solution. In situ Raman spectroscopy unveils the enhanced hydrogen evolution reaction mechanism of HEAAs. Overall, this work provides a novel chelating co-reduction strategy for the facile and versatile synthesis and design of advanced HEAAs and broadens the development and utilization of multi-elemental alloy electrocatalysts.
The main purpose of this study was to reveal the nutritional value and antioxidant activity of 34 edible flowers that grew in Yunnan Province, China, through a comprehensive assessment of their nutritional composition and antioxidant indices. The results showed that sample A3 of Asteraceae flowers had the highest total flavonoid content, with a value of 8.53%, and the maximum contents of vitamin C and reducing sugars were from Rosaceae sample R1 and Gentianaceae sample G3, with values of 143.80 mg/100 g and 7.82%, respectively. Samples R2 and R3 of Rosaceae were the top two flowers in terms of comprehensive nutritional quality. In addition, the antioxidant capacity of Rosaceae samples was evidently better than that of three others, in which Sample R1 had the maximum values in hydroxyl radical (·OH) scavenging and superoxide anion radical (·O2-) scavenging rates, and samples R2 and R3 showed a high total antioxidant capacity and 2,2-diphenyl-1-pyridylhydrazine (DPPH) scavenging rate, respectively. Taken together, there were significant differences in the nutrient contents and antioxidant properties of these 34 flowers, and the comprehensive quality of Rosaceae samples was generally better than the other three families. This study provides references for 34 edible flowers to be used as dietary supplements and important sources of natural antioxidants.
Antioxidants , Phenols , Humans , Antioxidants/chemistry , Phenols/chemistry , China , Flowers/chemistry , Flavonoids/chemistry , Plant Extracts/chemistry
